The video-based model assumes
access to shot-level TP labels. However, the only dataset for
TP identification we are aware of is TRIPOD
[1]}, which contains scene-level labels
based on screenplays. To obtain more fine-grained labels, we project
scene-based annotations to shots following a simple one-to-many
mapping (see Section  for details). Since
our training signal is unavoidably noisy, we hypothesize that access
to screenplays would encourage the video-based model to select shots
which are more representative for each TP. In other words, screenplays
represent privileged knowledge and an implicit supervision signal,
while alleviating the need for additional pre-processing during
inference. Moreover, screenplays provide a wealth of additional
information, e.g., about characters and their roles in a scene, or
their actions and emotions (conveyed by lines describing what the
camera sees). This information might otherwise be difficult to
accurately localize in video. Also, unlabeled text corpora of
screenplays are relatively easy to obtain and can be used to
pre-train our network.
